#install.packages("dplyr")
#install.packages("ggplot2")
#install.packages("ggraph")
#install.packages("igraph")
#install.packages("wordcloud")
#install.packages("zoom")
#library("plyr")
#library("dplyr")
#library("tidyr")
library(knitr)
opts_chunk$set(fig.path="images/",
cache.path="cache/",
cache=FALSE,
echo=TRUE,
message=FALSE,
warning=FALSE)
library("plyr")
library("dplyr")
library("tidyr")
#library("plyr")
Data<-read.csv("/Users/ranjitarajeevashetty/Documents/Summer_2018/RTBD_Project/Visualization/data_n.csv", header = TRUE,stringsAsFactors = FALSE)
#renameing
tech_node<- Data$Tech
tech_node<-as.data.frame(tech_node)
related_node<-Data$Related
related_node<- as.data.frame(related_node)
names(tech_node) <- c("id")
names(related_node)<- c("id")
#rbind
#total <- rbind(data frameA, data frameB)
total_nodes <- rbind(tech_node, related_node)
#deduped.data <- unique( yourdata[ , 1:3 ] )
### assuming df holds your data
#df.unique <- df[!duplicated(df$playerid), ]
total_nodes_deduped<-total_nodes[!duplicated(total_nodes), ]
total_nodes_deduped<-as.data.frame(total_nodes_deduped)
names(total_nodes_deduped)<- c("id")
#this is my node
names(total_nodes_deduped)[0]<-"group"
#total_nodes_deduped[-(1:2),][0]<-"group"
total_nodes_deduped$group<- "Data"
main_linkk<-Data%>%group_by(Tech,Related)%>% tally()%>%arrange(desc(n))
library(igraph)
data_ver<-read.csv("/Users/ranjitarajeevashetty/Documents/Summer_2018/RTBD_Project/Visualization/vertices_n.csv", header = TRUE,stringsAsFactors = FALSE)
main_linkk<-merge(x = main_linkk, y = data_ver, by.x= "Tech" , by.y ="Tech", all.x = TRUE )
#data_vertex<-data_vertex$Tech
data_net<- graph_from_data_frame(d=main_linkk, vertices=total_nodes_deduped, directed=T)
#V(data_net)$color[which(V(data_net)$Tech == "Data-2013")] <- 'blue'
library(visNetwork)
#library(dplyr)
library(igraph)
library(visNetwork)
visIgraph(data_net, layout ="layout_nicely")%>%visGroups(groupname = "Data", color = "deepskyblue", shape = "circle")%>%visOptions(highlightNearest = TRUE) %>%visLegend()%>%
visPhysics(stabilization = TRUE, solver = "barnesHut", barnesHut = list(gravitationalConstant = -5000, avoidOverlap=1))
library(ggplot2)
library(plotly)
LanguageTrend<- read.csv("/Users/ranjitarajeevashetty/Documents/Summer_2018/RTBD_Project/Visualization/LanguageTrend.csv", header = TRUE,stringsAsFactors = FALSE)
#ime vs Occupancy
plot_ly(LanguageTrend, x = ~Time_Period , y = ~word_count, name='',type = 'scatter', mode='lines',
color = ~Prog_lang)%>%layout(title=" Language Trend",xaxis=list(title="",tickangle=45 ),yaxis=list(title="Language"))
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.